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Record W2969303519 · doi:10.3138/ptc-2018-0101

Using Expert Consensus to Develop a Tool to Assess Physical Therapists’ Knowledge, Skills, and Judgement in Performing Airway Suctioning

2019· article· en· W2969303519 on OpenAlexaffvenueabout
Erin Miller, Dina Brooks, Brenda Mori

Bibliographic record

VenuePhysiotherapy Canada · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of TorontoMcMaster UniversityToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsJudgementDelphi methodDelphiMedicinePhysical therapyCardiorespiratory fitnessPsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: The aim of this study was to develop a tool to assess physical therapists’ knowledge, skills, and judgement in performing airway suctioning with intubated and non-intubated adults. Method: A modified Delphi methodology was used to develop the tool and to evaluate its sensibility (i.e., common-sense nature). Participants were experienced cardiorespiratory physical therapists who perform airway suctioning and physical therapists employed in academic positions related to cardiorespiratory physical therapy at Canadian universities. Round 1 focused on refining which items to include in the tool, Round 2 focused on finalizing the items, and Round 3 focused on evaluating a preliminary version of the tool. Results: A total of 34 individuals participated in Round 1, 30 participated in Round 2, and 25 participated in Round 3. A literature review identified 11 relevant domains and 69 supporting competencies. In Round 1, consensus was achieved for all domains; however, it was borderline for the professionalism domain. Multiple participants suggested that it was redundant because it is a global requirement for all physical therapists. Consensus was also achieved for 64 of the 69 supporting competencies; however, it was borderline for 5 of these items, and 5 achieved no consensus. In Round 2, participants rated a series of recommendations related to items requiring further consideration, as well as 9 new items suggested by the participants in Round 1. In Round 3, the preliminary tool was found to be globally sensible, but concerns were expressed about the inclusion of redundant factors and the tool’s length. The tool was revised, resulting in a tool with 4 domains, 6 sub-domains and 43 supporting competencies, as well as an item rating the individual’s overall performance. Conclusions: The final-round sensibility questionnaire provided preliminary evidence of the tool’s face and content validity. We will investigate the tool’s measurement properties in a future study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.446
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2019
Admission routes3
Has abstractyes

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